Reinforcement Learning and Robot Control
Reinforcement Learning (RL) is a technique where an agent learns to maximize rewards through interaction with its environment. When applied to industrial robots, complex tasks can be learned autonomously without explicit programming.
Limitations of Traditional Robot Programming
Reinforcement Learning Application Areas
Assembly Tasks
Learns part insertion and screw fastening tasks that require fine force control.
Bin Picking
Recognizes randomly stacked parts via camera and learns optimal grasping strategies.
Welding
Autonomously optimizes welding trajectory, speed, and current to improve weld quality.
Sim-to-Real Transfer
This technique transfers policies learned in simulation to real robots, enabling safe and rapid learning.
Conclusion
Reinforcement learning is a core technology driving the intelligence of industrial robots. Implement intelligent robot control with POLYGLOTSOFT's AI platform and WCS.